n. careddu

The noise of drilling in the dimension stone business is unbearable for both the workplace and the people who work there. In order to reduce the negative effects drilling has on the health of the environment, the drilling noise has to be measured, assessed, and controlled. The main purpose of this work is to investigate an experimental-intelligent method to predict the noise value of drilling in the dimension stone industry. For this purpose,135 laboratory tests are designed on five types of rocks (four types of hard rock and one type of soft rock), and their results are measured in the first step. In the second step, due to the unpredicted and uncertain issues in this case, artificial intelligence (AI) approaches are applied, and the modeling is conducted using three intelligent systems (IS), namely an adaptive neuro-fuzzy inference system-SCM (ANFIS-SCM), an adaptive neuro-fuzzy inference system-FCM (ANFIS-FCM), and the radial basis function network (RBF) neural network. 75% of the samples are considered for training, and the rest for testing. Several models are constructed, and the results indicate that although there is no significant difference between the models according to the performance indices, the proposed construction of ANFIS-SCM can be considered as an efficient tool in the evaluation of drilling noise. Finally, several scenarios are designed with different input modes, and the results obtained prove that the types of rock and the drill bits are more important than the operational characteristics of the machine.

Predicting the amperage consumption of cutting machines could be one of the critical steps in optimizing the energy-consuming points for the dimension stone cutting industry. Hence, the study of the relationship between the operational characteristics of cutting machines and rocks with focusing on the machine's energy-consuming is unavoidable. For this purpose, in the first step, laboratory studies under different operating conditions at different cutting depths and feed rates are performed on 12 soft and hard rock samples. In the continuation of the laboratory studies, the rock samples are transferred to the rock mechanics laboratory in order to determine the mechanical properties (uniaxial compressive strength and modulus of elasticity). The statistical studies are performed in the SPSS software in order to predict the electrical current consumption of the cutting machine according to the mechanical characteristics of the rock samples, cutting depth, and feed rate. The statistical models proposed in this work can be used with a high reliability in order to estimate the electrical current consumed in the cutting process.